Biology subjects

Zihua Wang

Publications and source records attributed to Zihua Wang.

3 recordsLinked to original sources

bioRxiv2016

MUMdex: MUM-based structural variation detection

MotivationStandard genome sequence alignment tools primarily designed to find one alignment per read have difficulty detecting inversion, translocation and large insertion and deletion (indel) events. Moreover, dedicated split read alignment methods that depend only upon the reference genome may misidentify or find too many potential split read alignments because of reference genome anomalies.\n\nMethodsWe introduce MUMdex, a Maximal Unique Match (MUM)-based genomic analysis software package consisting of a sequence aligner to the reference genome, a storage-indexing format and analysis software. Discordant reference alignments of MUMs are especially suitable for identifying inversion, translocation and large indel differences in unique regions. Extracted population databases are used as filters for flaws in the reference genome. We describe the concepts underlying MUM-based analysis, the software implementation and its usage.\n\nResultsWe demonstrate via simulation that the MUMdex aligner and alignment format are able to correctly detect and record genomic events. We characterize alignment performance and output file sizes for human whole genome data and compare to Bowtie 2 and the BAM format. Preliminary results demonstrate the practicality of the analysis approach by detecting de novo mutation candidates in human whole genome DNA sequence data from 510 families. We provide a population database of events from these families for use by others.\n\nAvailabilityhttp://mumdex.com/\n\nContactandrewsp@cshl.edu (or paa@drpa.us)\n\nSupplementary informationSupplementary data are available online.

Genomics↗

bioRxiv2015

Indel variant analysis of short-read sequencing data with Scalpel

As the second most common type of variations in the human genome, insertions and deletions (indels) have been linked to many diseases, but indels of more than a few bases are still challenging to discover from short-read sequencing data. Scalpel (http://scalpel.sourceforge.net) is open-source software for reliable indel detection based on the micro-assembly technique. To date, it has been successfully used to discover mutations in novel candidate genes for autism, and is extensively used in other large-scale studies of human diseases. This protocol gives an overview of the algorithm and describes how to use Scalpel to perform highly accurate indel calling from whole genome and exome sequencing data. We provide detailed instructions for an exemplary family-based de novo study, but we also characterize the other two supported modes of operation for single sample and somatic analysis. Indel normalization, visualization, and annotation of the mutations are also illustrated. Using a standard server, indel discovery and characterization in the exonic regions of the example sequencing data can be finished in ~6 hours after read mapping.

Bioinformatics↗

bioRxiv2013

Accurate detection of de novo and transmitted INDELs within exome-capture data using micro-assembly

We present a new open-source algorithm, Scalpel, for sensitive and specific discovery of INDELs in exome-capture data. By combining the power of mapping and assembly, Scalpel searches the de Bruijn graph for sequence paths (contigs) that span each exon. The algorithm creates a single path for exons with no INDEL, two paths for an exon with a heterozygous mutation, and multiple paths for more exotic variations. A detailed repeat composition analysis coupled with a self-tuning k-mer strategy allows Scalpel to outperform other state-of-the-art approaches for INDEL discovery. We extensively compared Scalpel with a battery of >10000 simulated and >1000 experimentally validated INDELs between 1 and 100bp against two recent algorithms for INDEL discovery: GATK HaplotypeCaller and SOAPindel. We report anomalies for these tools in their ability to detect INDELs, especially in regions containing near-perfect repeats which contribute to high false positive rates. In contrast, Scalpel demonstrates superior specificity while maintaining high sensitivity. We also present a large-scale application of Scalpel for detecting de novo and transmitted INDELs in 593 families with autistic children from the Simons Simplex Collection. Scalpel demonstrates enhanced power to detect long ([≥]20bp) transmitted events, and strengthens previous reports of enrichment for de novo likely gene-disrupting INDEL mutations in children with autism with many new candidate genes. The source code and documentation for the algorithm is available at http://scalpel.sourceforge.net.

Bioinformatics↗